Revisiting the predictability of follicular fluid leptin and related adiposity measures for live birth in women scheduled for ICSI cycles, a prospective cohort study.

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Abdelmagied A;, Alaa A. Makhlouf, Ahmed A. Abdel-Aleem, Safwat A. Mohamed, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3437245/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2024 Read the published version in Middle East Fertility Society Journal → Version 1 posted 5 You are reading this latest preprint version Abstract Background Our research question is; could follicular fluid (FF) leptin solely or contemporaneously with other clinical, biochemical and sonographic adiposity measures predict the probability of having a live birth during ICSI cycles? .This is a prospective cohort study that enrolled infertile women without polycystic ovary syndrome scheduled for ICSI. At baseline, women had assessment of obesity using different metrics: clinical, serum biochemical, and sonographic. Clinical measures encompassed waist circumference and body mass index. Biochemical evaluation comprised assessment of homeostasis-model for insulin resistance, visceral adiposity index and lipid accumulation product. Preperitoneal and subcutaneous abdominal fat were measured using ultrasound and body fat index was calculated. On day of oocyte retrieval, pooled FF was sampled to assess FF leptin. Our primary outcome was live birth after one fresh embryo transfer cycle. Results Out of Ninty-one women analyzed in this study, 28 have a live birth (30.8%). No difference in FF leptin concentration was found between women with and without live birth (Mean ± SD; 20336 ± 8006 vs 18493 ± 6655 pg/ml; P = 0.2). None of the assessed adiposity markers was a predictor for live birth. Substantially, follicular fluid leptin was positively correlated with insulin resistance in women with and without live birth (r = 0.21, P = 0.04). In logistic regression analysis, the outcome of the prior cycle, ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for live birth in our study. Conclusions The present work could not find evidence that follicular fluid leptin, preperitoneal fat and other evaluated adiposity measures could impact live birth after ICSI cycles. Body fat index central obesity follicular fluid leptin ICSI insulin resistance preperitoneal fat Figures Figure 1 Figure 2 Background In reproductive-aged women, obesity has been linked, in addition to the metabolic health risks, to ovulatory dysfunction, menstrual irregularities and suboptimal outcomes of variant fertility treatments (Practice Committee of the American Society for Reproductive Medicine, 2021 ; Gonzalez et al., 2022 ) Clinical metrics utilized as crucial descriptors for obesity include body mass index (BMI) and waist circumference (WC). However, body mass index considers total body weight and height without referring to body fat or central (abdominal) obesity. Also, waist circumference, although could accurately identify women with central obesity, it cannot precisely reflect preperitoneal fat. (Ponti et al., 2020 ) Hence, different imaging tools for measuring preperitoneal fat have been proposed in reproductive aged women. Ultrasound has been determined as valid tool for assessment of intraabdominal preperitoneal fat compared to computerized tomography scan and magnetic resonance imaging. Moreover, ultrasound is more familiar in the clinicians' hands, inexpensive and devoid of radiation hazards (Suzuki et al., 1993 ; Stolk et al., 2001 ; Ribeiro-Filho et al., 2001 ; Hamagawa et al., 2010 ) An earlier report demonstrated that preperitoneal fat thickness by ultrasound was superior to subcutaneous fat in predicting insulin resistance and other metabolic syndrome elements in non-specific population (Meriño-Ibarra et al., 2005 ). At the level of the ovary, Ciavattini and colleagues found that increased preperitoneal fat as measured by ultrasound was associated with high reactive oxygen species in the follicular fluid and negatively correlated with oocyte and embryo quality (Ciavattini et al., 2017 ). Moreover, Obese anovulatory women with polycystic ovary syndrome (PCOS) who resume ovulation during a 6-month lifestyle program lost more visceral fat compared to the women who did not resume ovulation; despite similar subcutaneous fat loss in both groups (Kuchenbecker et al., 2011 ). Leptin is a product of the adipose tissue. It is a hormone that plays a key role in the regulation of HPO axis to start puberty and maintain ovarian function (Silvestris et al., 2018 ). In infertile women scheduled for In-vitro-fertilization (IVF), there are conflicting reports regarding the effect of follicular fluid (FF) leptin level on IVF outcomes. Some suggested deleterious effect of high leptin on embryo quality (Polyzos et al., 2022 ). This controversy is evident in studies recruiting non-PCOS infertile population. Heterogenous methodology and endpoints stand beyond these incongruent observations. These studies did not analyze or correlate the findings in relation to different adiposity measures such as the preperitoneal fat, body fat index, or visceral adiposity index. And, few of them reported correlation with insulin resistance (Llaneza-Suarez et al., 2014 ). Neither body fat index (BFI) nor visceral adiposity index (VAI) have been assessed before in non-PCOS women for IVF. Body fat index that compiled preperitoneal and subcutaneous fat in its calculation is a newly studied index in pregnant women as a predictability tool for gestational diabetes. (Nassr et al., 2018 ; Benchahong et al., 2023 ; Singh et al., 2023 ), while visceral adiposity index has been deemed to predict insulin resistance in PCOS women (Oh et al., 2013 ). In the current study, the researches targeted infertile women without PCOS to revisit the hypothesis whether FF leptin as adiposity biomarker could predict intracytoplasmic sperm injection (ICSI) outcomes in these women. Moreover, we integrated other adiposity measures in our evaluation (clinical, biochemical as well as sonographic) that have been proposed to be leptin-related. The reason for excluding PCOS women is to avoid the confounding effect of the pathophysiological mechanisms of PCOS on the study outcomes. Materials and methods Study design and setting: Our study is a prospective cohort study, conducted at Assisted Conception Unit, Department of Obstetrics and Gynecology, Women's Health Hospital, Assiut University, Egypt. The study was registered (NCT03778684, www.clinicaltrials.gov ). Recruitment was started on February 2019, and the study was completed on November 2022. Study participants: Infertile women indicated for intracytoplasmic sperm injection (ICSI) were eligible for enrollment if they were non-PCOS, aged between 18 and 35 years, anticipated normal responders, and had normal uterine cavity by trans-vaginal ultrasound. Non-PCOS women scheduled for ICSI comprised women with anovulation, unexplained infertility, tubal disease and male factor. Women with PCOS, those administering metformin, diabetic women, and poor responders based on Bologna criteria (Ferraretti et al., 2011 ) were not included in the study. The Rotterdam European Society for Human Reproduction and Embryology (ESHRE)/American Society for Reproductive Medicine (ASRM) criteria were used to define PCOS (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group, 2004). Only one fresh ICSI transfer cycle for each participant was analyzed for the study outcomes. Sample size calculation: Based on a study by Llaneza-Suarez and colleagues (Llaneza-Suarez et al., 2014 ) who determined a mean FF leptin concentration of 16.8 ng/mL (SD ± 6.0 ng/ml), and 11.5 ng/ml (SD ± 4.6 ng/ml) in non-PCOS women without live birth and with live birth respectively, 50 women were required as a sample size at 85% study power, two-sided significance level of 0.05, and effect size of 0.88. Owing to the large effect size, we were willing to test the hypothesis at a modest effect size of 0.65 at the same power and significance level, so the concluded sample size was 88 non-PCO women. Sample size calculation was done using G-Power 3.1.9.2 software program. Evaluated adiposity measures: Clinical (BMI and WC), biochemical (VAI, lipid accumulation product, insulin resistance, and follicular fluid leptin) and sonographic (BFI) obesity-realted parameters were assessed. Clinical measures : They were evaluated at baseline and comprised waist circumference and body mass index. Waist circumference was measured by at the end of expiration by a tape applied to the skin of the participant at a plane perpendicular to the midline and passing through a point just above the top of the iliac crest (National Institutes of Health, 2000 ). For body mass index (BMI), weight and height were measured while the participant is standing and wearing neither more than one layer of light clothes nor shoes. Biochemical measures in serum and follicular fluid : Before starting any ovarian stimulation, serum samples were taken following overnight fast for assessment of serum glucose, serum insulin and serum lipoproteins. Serum Glucose level was measured in mmol/L using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA. Serum fasting Insulin were measured in µIU/mL using Bioscience Human Insulin ELISA Kit (Catalog number :10801). Serum triglycerides and High-density lipoprotein cholesterol (HDL-C) levels were measured using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA. On day of oocyte retrieval, follicular fluid pooled from large follicles; 17 mm or more, containing cumulus-oocyte complex was selected for sampling. Fluids containing debris and blood were excluded. They were centrifugated at 1500 rpm for 5 minutes, then stored at -80 C until leptin measurement was performed using SinoGeneClon ELISA Kit (Catalog number: SG-10057). Follicular fluid leptin determination was by pg/ml. All biochemical tests were performed in the laboratory of Women's Health Hospital, Assiut University, Egypt. Sonographic measures: We measured in this study the abdominal subcutaneous and preperitoneal fat utilizing the methodology validated in literature using ultrasound (Suzuki et al., 1993 ; Stolk et al., 2001 ; Ribeiro-Filho et al., 2001 ; Hamagawa et al., 2010 ; Ciavattini et al., 2017 ; Nassr et al., 2018 ). The maximum preperitoneal and the minimum subcutaneous fat were the target measurements. They were conducted by the same researcher (The third author: AAM) using SONOACE R5 ultrasound machine with CN2-8 curved abdominal transducer (Samsung Medison Co., LTD). Four-months duration of capacity building for the researcher was achieved before study proposal submission to IRB through coupling with level-3 experience sonographer in order to ensure and maximize the quality of the scans. Calculations and benchmarking: Body mass index was calculated as weight (kg) divided by square of height (m 2 ). According to World Health Organization (WHO), BMI was categorized as normal (18.5–24.9 kg/m 2 ), overweight (25- 29.9 kg/m 2 ), or Obese (30 and above kg/m 2 ) (World Health Organization, 2014 ). To identify women with central obesity, waist circumference equal or more than 80 cm was used according to the International Diabetes Federation (IDF) and the report of WHO Expert Consultation on Obesity. (World Health Organization, 2000 ; 2011 ; Alberti et al, 2007 ). The homeostasis-model assessment for insulin resistance (HOMA-IR) was calculated using the equation: fasting insulin (µIU/mL) x glucose (mmol/L)) /22.5. Participants were designated to be insulin resistant if HOMA-IR was equal or more than 2.5 (Matthews et al., 1985 ; Bo et al., 2012 ). Nassr et al ( 2018 ) was the first to conclude and report on the body fat index formula. We utilized the same formula to calculate that new adiposity marker. It was calculated by multiplying pre-peritoneal fat (mm) and subcutaneous fat (mm), then dividing the product by height (cm) (Nassr et al., 2018 ) Sex-specific equations were employed to calculate visceral adiposity index and lipid accumulation product (Amato et al., 2010 ; Taverna et al., 2011 ). Cycle management, Ovarian stimulation protocol and Embryo transfer: For each woman, personal and fertility data were reported, and the indication for ICSI was affirmed. Preparation of the patients and selection of the ovarian stimulation protocol followed the standardized protocols in ICSI practice and was individualized based on each patient characteristics. Number of transferred embryos and day of embryo transfer were not uniform for all enrolled patients, nevertheless the same clinician conducted all transfers. Study endpoints The primary endpoint of the study was the probability of having a live birth (LB) per aspirated cycle as defined by the delivery of a live baby at 28 weeks of gestation or more. This is considered the standard definition in Egypt. We followed the standards of the Core Outcome Measure for Infertility Trials (COMMIT) initiative in reporting the primary and secondary endpoints (Duffy et al., 2020 ). The Time frame for reporting the outcomes was one fresh embryo transfer cycle. Enrolled women were contacted at the time of pregnancy test and estimated delivery date. Statistical analysis: Statistical analysis was performed with the use of SPSS statistical package version 26.0 (IBM Corp, Armonk, NY, USA). The Kolmogorov-Smirnov test was used to determine data distribution. Normally distributed data are presented as mean (SD) however, abnormally distributed variables are presented as median (interquartile range (IQR)). Comparisons were conducted between women with and without live birth. Also, comparisons were done between women with and without central obesity as well as among the common three indications of ICSI in our cohort; unexplained, male, and other factors in order to explore if there was any hidden effect of the indication of ICSI on the study variables and outcomes. Other factors encompass tubal disease, endometriosis, anovulation, and combined factors. Based on the comparisons, when appropriate, means were compared with the use of Student t test or One-way ANOVA, and medians of non-parametric variables were compared utilizing Mann Whitney U test or Kruskal-Wallis test. Correlation analysis was conducted to determine correlation between the adiposity measures and ICSI cycle variables and outcomes. Predictive models were constructed using regression and receiver operating characteristic (ROC) curve analyses to evaluate the predictability of adiposity measures for cycle outcomes. P value of < 0.05 was considered to be statistically significant. Results Ninety-one women were enrolled and completed the study. Their median (IQR) age was 30 (7) years with 54.9% of the cohort (n = 50) were in their thirties. In our cohort, the common indications for ICSI were male and unexplained factors in 41.8% (n = 38) and 36.3% (n = 33) of women respectively. Other indications of ICSI included tubal (9.9%), anovulatory (4.4%), endometriosis (1.1%), and combined (6.6%) factors. History of ICSI was found in 25 (27.5%) women while the remainder was undergoing their first ICSI cycle. Antagonist protocol was chosen for about two-thirds of the participants (n = 60;65.9%). Eleven women (12.1%) had normal BMI while 33 (36.3%) and 47 (51.6%) were overweight and obese respectively. Based on waist circumference 52 (57.1%) women had central obesity. The total pregnancies were 31 (34.1%) ended in 28 live births: 25 term and 3 preterm births. There was one first trimester miscarriage, one second trimester miscarriage, and one ectopic pregnancy. Of live births, 11 cases were multiple pregnancies. Based on the primary endpoint (live birth), comparisons between women with (n = 28) and without live birth (n = 63) are shown in Tables 1 and 2 . Neither follicular fluid leptin concentration nor other adiposity measures were different between women with and without live birth. In Receiver Operating characteristic curve (ROC) and logistic regression analyses, none of the evaluated adiposity measures (WC, BMI, BFI, VAI, LAP, HOMA-IR, and FF Leptin) in our study was predictor for having a live birth. Area under the curve for WC, BMI, BFI, VAI, LAP, HOMA-IR, and Follicular fluid leptin was 0.57, 0.54, 0.55, 0.51, 0.53, 0.53, and 0.56, respectively. Stepwise multivariable logistic regression analysis, showed that the outcome of the prior cycle, ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for having live birth in our study (Table 3 ). Investigating the relation among adiposity markers indicated that follicular fluid leptin was only correlated with HOMA-IR (Spearman's Correlation coefficient r = 0.21, P = 0.04) (Fig. 1 ). This correlation is corroborated by the finding that HOMA-IR tends to be higher with increasing leptin tertile (P = 0.047), when categorizing leptin values of the studied cohort into 3 tertiles: the first tertile is: 22130.9 (n = 30 women). Loess regression with Epanechnikov kernel fitting was done to demonstrate the interaction of follicular fluid leptin and HOMA-IR on live birth (Fig. 2 ). The scatter plot indicates that leptin increases with increased insulin resistance both in women with and without live birth. However, surprisingly in both groups, this positive correlation was lost or even reversed when HOMA-IR approached 5 or more. Comparing women with central obesity to their counterparts show that they were obese and overweight, respectively (median (IQR): 32 (6.9) vs 27.1 (6); P < 0.001). Both groups were comparable regarding insulin resistance, follicular fluid leptin concentration, ICSI cycle characteristics and outcomes. Miscarriage cases occurred in women without central obesity while all preterm deliveries were reported in central obesity women. However, in deed, we found central obesity women more likely to have higher BFI (p < 0.001), VAI (p < 0.001), and LAP (p < 0.001) (Supplemental tables 1 and 2). Unplanned subgroup post-hoc analysis according to the indication of ICSI showed that women in the male factor group had the lowest BMI (p = 0.04), and through borderline significance; the least preperitoneal fat thickness (p = 0.05), and the lowest fertilization rate (p = 0.06). All cancelled transfers (n = 4) were also in male factor group. Yet, follicular fluid leptin, the rest of the adiposity markers and cycle parameters as well as outcomes did not differ among women in this subgroup analysis (Supplemental tables 3 and 4). Table 1 Baseline characteristics and adiposity measures of women with and without LB: Variable Women with LB (n = 28) Women without LB (n = 63) P value Age (years) (Median, IQR) 29.5 (9.25) 30 (7) 0.2 Age (years) (n,%) 0.6 18- <25 7 (25%) 10 (15.9%) 25- <30 7 (25%) 17 (27%) 30–35 14 (50%) 36 (57.1%) AMH (Median, IQR) 2.2 (1.14) 2.2 (1.89) 0.9 Prior ICSI cycles (n, %) 0.004 Prior failed cycle 2 (7.1%) 12 (19%) Prior successful cycle 8 (28.6%) 3 (4.8%) First cycle 18 (64.3%) 48 (76.2%) Causes of infertility (n,%) 0.9 Male factor 12 (42.9%) 26 (41.3%) Unexplained 10 (35.7%) 23 (36.5%) Tubal factor 2 (7.1%) 7 (11.1%) Anovulation 2 (7.1%) 2 (3.2%) Endometriosis Zero 1 (1.6%) Combined 2 (7.1%) 4 (6.3%) BMI (kg/m 2 ) (Median, IQR) 31 (6.9) 29 (8) 0.5 Class of body mass index (n, %) 0.4 Normal 2 (7.1%) 9 (14.3%) Overweight 9 (32.1%) 24 (38.1%) Obese 17 (60.7%) 30 (47.6%) Waist Circumference in cm (Median, IQR) (Range) 90 (22.8) 55–125 85 (22) 55–115 0.3 % of women with central obesity (n,%) 15 (53.6%) 37 (58.7%) 0.6 % of insulin resistant women (≥ 2.5) (n,%) 14 (50%) 33 (52.4%) 0.8 HOMAIR (Median, IQR) 2.6 (3.2) 3.3 (3.3) 0.6 Preperitoneal fat in mm (Median, IQR) (Range) 10 (5.9) (6–18.9) 11 (5.5) (6.2–20) 0.5 Subcutaneous fat in mm (Median, IQR) (Range) 20.5 (8.7) (10.4–33.7) 20.7 (14.7) (6.90–42) 0.4 Body fat index (Median, IQR) 1.35 (1.5) 1.24 (1.84) 0.4 Visceral adiposity index (Median, IQR) 1.44 (0.9) 1.42 (0.74) 0.9 Lipid accumulation product (Median, IQR) 35.2 (23.24) 28.4 (29.84) 0.6 Follicular fluid Leptin* in pg/ml (Mean ± SD) 20336 ± 8006 18493 ± 6655 0.2 *Results of follicular fluid samples for 6 cases (4 in women with LB group, and 2 in their counterparts) showed minimally than detected levels that were attributed to error in sampling or storage. So, leptin values for these 6 cases were computed through linear interpolation method for missed data calculation. HOMA-IR: homeostasis-model assessment for insulin resistance, BMI: body mass index, AMH: Anti-mullerian hormone. Table 2 ICSI Cycle characteristics and outcomes in women with and without LB: Variable Women with LB (n = 28) Women without LB (n = 63) P value Antral follicular count (Median, IQR) 12 (6) 12 (6) 0.9 Toral gonadotropins dose (Median, IQR) 3450 (1425) 3600 (1500) 0.6 Total days of stimulation (Median, IQR) 11 (1) 12 (2) 0.5 Peak E2 (pg/ml) * (Median, IQR) 2618 (1636) 2738 (1125) 0.9 Day of trigger Progesterone (ng/ml) ** (Median, IQR) 0.94 (0.57) 0.95 (0.44) 0.8 Day of trigger Endometrial thickness (mm) (Median, IQR) (Range) 10 (2) (8–13) 10 (2) (7–14) 0.5 Oocyte retrieved (Median, IQR) 11 (9.5) 12 (7) 0.6 Metaphase II oocytes (Median, IQR) 9.5 (8.5) 8 (9) 0.1 Embryo transfer (n, %) One Embryo Two Embryos Three Embryos Four Embryos Cancelled Transfer 0 12 (42.9%) 16 (57.1%) 0 0 4 (6.3%) 25 (39.7%) 25 (39.7%) 5 (7.9%) 4 (6.3%) 0.1 Maturation index in % 84.7% 76.4% 0.02 Fertilization rate in % 75% 66.7% 0.1 Women with good quality embryo transfer (at least one) (n,%) 27 (96.4%) 46 (73%) 0.03 Women who cryopreserved (n, %) 19 (67.9%) 18 (30.5%) 0.001 Peak E2 *, and Progesterone ** were analyzed for 77 and 65 cases, respectively due to missing data Table 3 Predictability of cycle parameters for having a live birth ICSI cycle parameter P value OR 95% CI Prior cycle result 0.028 2 1.07–3.72 The ability to yield good quality Embryos 0.279 0.55 0.18–1.63 The ability to have cryopreserved embryos < 0.001 5.28 2.01–13.83 Oocyte maturation index 0.044 22.16 1.09–451.24 Discussion Main findings: Employing a prospective cohort study design, this study examined the follicular fluid leptin, along with a group of feasible clinical, biochemical, and sonographic adiposity markers as predictors of ICSI cycle outcomes in infertile non-PCOS women. Follicular fluid leptin was the basis for power analysis of the sample size. According to BMI, only 12.1% of women had normal BMI while the remainder is overweight and obese. None of the tested parameters (including follicular fluid leptin levels) could predict a live birth. Insulin resistance was the only adiposity marker that has been positively correlated to follicular fluid leptin. Strengths: Only FF leptin and insulin resistance were tested before in women without polycystic ovary syndrome. In the present work, we tested multiple markers that never were evaluated before in such patient cohort particularly; preperitoneal fat, body fat index and visceral adiposity index. Multiplicity of the evaluated markers was to seek explanations, detect superiority, and build combined predictors, in case of significance. The concept of testing multiple variants of predictors in overweight and obese women comes from observations that sole utility of leptin is not adequate to construct predictive model. Leptin to body mass index ratio has been reported to be superior to leptin alone as an IVF outcome predictor (Brannian et al., 2001 ). Dearth of literature reported live birth as a primary endpoint for the impact of FF leptin on ICSI. We followed our participants till delivery to reflect on live birth concurring with the published standards for reporting infertility trials (Harbin Consensus Conference Workshop Group, 2014 ; Duffy et al., 2020 ). Limitations: There are limitations that should be pointed out. First, underrepresentation of normal BMI women in the enrolled cohort, which could affect the generalizability of conclusions and mitigate the discriminative threshold of the evaluated indices. Second, follicular fluid sampling like other studies presents a limitation (Jafarpour et al., 2021 ). We used pooled follicular fluid which is still better than sampling just the first follicle. The ideal is to sample fluid from each follicle to conduct follicle-to-embryo tracking and sibling oocyte cohort analysis. However, it is difficult from the implementation point of view. Third, the study was powered for detection of leptin predictability but not for other tested parameters. Lack of comparable studies with similar study design presents a challenge in proposing assumptions during sample size calculation. Thus, the conclusions for the other parameters should be taken with caution. Fourth, although excluding PCOS women was justifiable, the performance of these indices to predict cycle outcomes in PCOS women remain elusive. Comment on the study adiposity measures: Leptin is a neuroendocrinal protein that exists in excess in obese women. Integrating follicular fluid leptin in our study is rationalized by; First: leptin is a reflection of the oocyte microenvironment; Second: leptin receptors and m-RNA are expressed in granulosa cells (Löffler et al., 2001 ), oocytes (Cioffi et al., 1997 ; Antczak and Van Blerkom, 1997 ) and pre-implantation embryos (Antczak and Van Blerkom, 1997 ); Third: leptin is depicted to regulate ovarian steroidogenesis, follicular growth and apoptosis, and oocyte maturation (Craig et al., 2004 ; Sirotkin et al., 2008 ); Fourth: controversial reports exist for the impact of FF leptin on cycle outcomes in the studied infertile cohort (Mantzoros et al., 2000 ; Anifandis et al., 2005 ; Jafarpour et al., 2021 ). Our results concur with the results of a recent metanalysis evaluating (Jafarpour et al., 2021 ) 11 observational studies (266 pregnant and 552 non-pregnant cases). This metanalysis indicated that pregnancy was unrelated to leptin levels in the follicular fluid. Highlighting the dilemma of methodological heterogenicity in follicular fluid sampling and cycle outcome reporting, different groups of researches conveyed favorable cycle outcomes with low follicular fluid leptin concentrations. Mantzoros et al. ( 2000 ) sampled fluid from the first aspiration of the dominant follicle and demonstrated low FF leptin in pregnant women (11.9 vs 17 ng/ml) within three ART cycles. However, this could bias their conclusions as comparable follicular fluid microenvironment among all follicles cannot be guaranteed. Another study by Anifandis et al ( 2005 ) did not explore the sampling details of follicular fluid and found women with peak estradiol range 1001–2000 (n = 53) yielded the highest pregnancy rate (35.8%) and had the lowest follicular fluid concentration (52 ng/ml). But, they did not test the predictability of follicular fluid leptin for clinical pregnancy after adjustment of peak serum estradiol. A study reporting live birth as an outcome found FF leptin sampled from the first dominant follicle predicted live birth at a concentration of 16 ng/ml with sensitivity and specificity of 78.3% and 54.2%, respectively (Llaneza-Suarez et al., 2014 ). Another group has addressed higher sensitivity and specificity of similar leptin concertation for the oocyte maturation when follicular fluid was pooled from the largest 3 follicles. (Hong et al., 2022 ). Body mass index has been the benchmarking for decades in IVF/ICSI daily practice in pre-IVF/ICSI preparation of obese/overweight infertile women to achieve the optimum outcomes for the service stakeholders (Fertility NICE, 2013 ; ESHRE Guideline Group on Ovarian Stimulation, 2020). The contradictory conclusions about the effect of body mass index and weight reduction on the IVF/ICSI outcomes (Rittenberg et al., 2011 ; Mutsaerts et al., 2016 ; Einarsson et al., 2017 ; Sermondade et al., 2019 ; Tang et al., 2021 ; Kim et al., 2021 ) should encourage the clinicians to test other or new feasible adiposity predictors for the cycle outcomes such as visceral adiposity and body fat indices. Obesity and abdominal preperitoneal fat alter the metabolic milieu of follicular fluid in IVF women by increasing lipid peroxides and reducing total antioxidant capacity (Nasiri et al., 2015 ; Hauck and Bernlohr, 2016 ; Ciavattini et al., 2017 ; Bacchetti et al., 2019 ). Reflection of the preperitoneal fat on FF leptin in not addressed before. In our results, neither preperitoneal fat nor the new body fat index improved the predictability of FF leptin for cycle outcomes. Body fat index has been evaluated before in pregnant women and revealed to predict gestational diabetes at cut offs of 0.5 and 0.88 (Nassr et al., 2018 ; Benchahong et al., 2023 ; Singh et al., 2023 ) . The concept of preperitoneal fat measurement is warranted by evidence that it is a source of multiple inflammatory mediators contributing to insulin resistance and reproductive dysfunction (Xu et al., 2003 ; Fischer-Posovszky et al., 2007 ; Lumeng and Saltiel, 2011 ). The correlation between insulin resistance and leptin found in our study is observed in other studies (Llaneza-Suarez et al., 2014 ) and supported by the fact that insulin stimulates leptin secretion (Catteau et al., 2016 ). This correlation was unrelated to live birth in our women. Moreover, surprisingly, the increase in leptin with insulin resistance was lost at higher insulin resistance level denoting that a phenomenon of desensitization could be exhibited by leptin receptors at high insulin resistance levels. Further research is needed to elucidate this finding. Conclusion The current work could not find evidence that a correlation exists between follicular fluid leptin and live birth after ICSI cycles. Furthermore, prediction for live birth after IVF, could not be demonstrated when any of the tested parameters (clinical, biochemical, preperitoneal fat and BFI) was used. Further research should be warranted to evaluate the plausibility of these indices in all BMI categories of women without PCOS. Declarations Ethics approval and consent to participate: The Institutional Review Board (IRB) of Faculty of Medicine, Assiut University approved the study on January, 22, 2019 (IRB approval number: 17200286). Every patient was informed about the steps of the study and a written informed consent was obtained from each patient. Availability of data and materials : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests : the authors declare that they have no competing interests. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors ' contribution: (1) the conception and design of the study or acquisition of data, or analysis and interpretation of data: Ahmed Abdelmagied,Mohammed K. Ali, Safwat Abdel-Rady, Ahmed A. Abdel-Alleem, Alaa Makhlouf, Azza Abo Elfadl (2) drafting the article or revising it critically for important intellectual content: Ahmed Abdelmagied, Mohammed K. Ali (3) final approval of the version to be submitted: Ahmed Abdelmagied, Alaa Makhlouf, Ahmed A. Abdel-Aleem, Safwat A. Mohamed, Azza Abo Elfadl , Mohammed K. Ali Acknowledgments : The authors would like to thank Prof. Dina Habib for her role in the capacity building for the second author AAM before study start. Also, we would like thank Prof. Ehab Soud for his sincere linguistic revision of the manuscript. 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Metabolism. 2005; 54(9): 1230-1235. Mutsaerts, M.A., Van Oers, A.M., Groen, H., Burggraaff, J.M., Kuchenbecker, W.K., Perquin, D.A., Koks, C.A., Van Golde, R., Kaaijk, E.M., Schierbeek, J.M., Oosterhuis, G.J. Randomized trial of a lifestyle program in obese infertile women. New England Journal of Medicine. 2016; 374(20): 942-1953. Nassr, A. A., Shazly, S. A., Trinidad, M. C., El-Nashar, S. A., Marroquin, A. M., Brost, B. C. Body fat index: a novel alternative to body mass index for prediction of gestational diabetes and hypertensive disorders in pregnancy. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2018; 228: 243-248 Nasiri, N., Moini, A., Eftekhari-Yazdi, P., Karimian, L., Salman-Yazdi, R., Zolfaghari, Z., Arabipoor, A. Abdominal obesity can induce both systemic and follicular fluid oxidative stress independent from polycystic ovary syndrome. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2015; 184: 112-116 National Institutes of Health, The Practical Guide Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, 2000 Oh, J. Y., Sung, Y. A., Lee, H. J. The visceral adiposity index as a predictor of insulin resistance in young women with polycystic ovary syndrome. Obesity. 2013; 21(8): 1690-1694. Polyzos, N. P., Ayoubi, J. M., Pirtea, P. General infertility workup in times of high assisted reproductive technology efficacy. Fertility and Sterility. 2022; 118(1): 8-18 Ponti, F., De Cinque, A., Fazio, N., Napoli, A., Guglielmi, G., Bazzocchi, A. Ultrasound imaging, a stethoscope for body composition assessment. Quant Imaging Med Surg. 2020;10(8):1699-1722. Practice Committee of the American Society for Reproductive Medicine. Obesity and reproduction: a committee opinion. Fertil Steril. 2021;116(5):1266-1285. doi: 10.1016/j.fertnstert.2021.08.018. Epub 2021 Sep 25. PMID: 34583840. Ribeiro-Filho, F.F., Faria, A.N., Kohlmann Jr, O., Ajzen, S., Ribeiro, A.B., Zanella, M.T., Ferreira, S.R. Ultrasonography for the evaluation of visceral fat and cardiovascular risk. Hypertension. 2001; 38(3): 713-717. Rittenberg, V., Seshadri, S., Sunkara, S. K., Sobaleva, S., Oteng-Ntim, E., El-Toukhy, T. Effect of body mass index on IVF treatment outcome: an updated systematic review and meta-analysis. Reproductive biomedicine online. 2011; 23(4), 421-439. Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod. 2004;19(1):41-7. doi: 10.1093/humrep/deh098. PMID: 14688154. Sermondade, N., Huberlant, S., Bourhis-Lefebvre, V., Arbo, E., Gallot, V., Colombani, M., Fréour, T. Female obesity is negatively associated with live birth rate following IVF: a systematic review and meta-analysis. Human reproduction update. 2019; 25(4): 439-451. Silvestris, E., De Pergola, G., Rosania, R., Loverro, G. Obesity as disruptor of the female fertility. Reproductive Biology and Endocrinology. 2018; 16: 1-13. Singh, D., Mittal, P., Bachani, S., Mukherjee, B., Mittal, M.K., Suri, J. Ultrasonographic Assessment of Body Fat Index for Prediction of Gestational Diabetes Mellitus and neonatal complications. J Obstet Gynaecol Can. 2023; S1701-2163(23)00449-8. doi: 10.1016/j.jogc.2023.04.026. Epub ahead of print. PMID: 37437777. Sirotkin, A.V., Mlynček, M., Makarevick, A.V., Florkovičová, I., Hetényi, L. Leptin affects proliferation-, apoptosis-and protein kinase A-related peptides in human ovarian granulosa cells. Physiological Research. 2008; 57(3). Stolk, R.P., Wink, O., Zelissen, P.M.J., Meijer, R., Van Gils, A.P.G., Grobbee, D.E. Validity and reproducibility of ultrasonography for the measurement of intra-abdominal adipose tissue. International journal of obesity. 2001; 25(9): 1346-1351. Suzuki, R., Watanabe, S., Hirai, Y., Akiyama, K., Nishide, T., Matsushima, Y., Murayama, H., Ohshima, H., Shinomiya, M., Shirai, K., Saito, Y. Abdominal wall fat index, estimated by ultrasonography, for assessment of the ratio of visceral fat to subcutaneous fat in the abdomen. The American journal of medicine. 1993; 95(3): 309-314. Tang, K., Guo, Y., Wu, L., Luo, Y., Gong, B., Feng, L. A non-linear dose-response relation of female body mass index and in vitro fertilization outcomes. Journal of Assisted Reproduction and Genetics. 2021; 38: 931-939. Taverna, M.J., Martínez-Larrad, M.T., Frechtel, G.D., Serrano-Ríos, M. Lipid accumulation product: a powerful marker of metabolic syndrome in healthy population. European journal of endocrinology. 2011; 164(4):559-67. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. (TRS 894). Geneva, World Health Organization (WHO), 2000 World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, World Health Organization, 2011. World Health Organization. Global status report on noncommunicable diseases. World Health Organization, 2014. https://apps.who.int/iris/handle/10665/148114 Xu, H., Barnes, G.T., Yang, Q., Tan, G., Yang, D., Chou, C.J., Sole, J., Nichols, A., Ross, J.S., Tartaglia, L.A., Chen, H. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. The Journal of clinical investigation. 2003;112(12):1821-30. Supplementary Files Supplementray.docx Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2024 Read the published version in Middle East Fertility Society Journal → Version 1 posted Reviewers agreed at journal 04 Nov, 2023 Reviewers invited by journal 01 Nov, 2023 Editor invited by journal 23 Oct, 2023 Editor assigned by journal 16 Oct, 2023 First submitted to journal 13 Oct, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3437245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245341468,"identity":"0e80cea3-939d-498e-b4a7-e307b5c0afd1","order_by":0,"name":"Abdelmagied A;","email":"","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdelmagied","middleName":"","lastName":"A;","suffix":""},{"id":245341469,"identity":"2f51b57e-138d-43bc-8e77-08a1e127691e","order_by":1,"name":"Alaa A. 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Ali","email":"","orcid":"","institution":"Assiut University Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"K.","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2023-10-12 11:51:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3437245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3437245/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43043-024-00164-y","type":"published","date":"2024-01-17T15:01:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":46029135,"identity":"fc1b57b6-bd23-4f8c-84f6-1dcff6bd4f11","added_by":"auto","created_at":"2023-11-07 17:48:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":29142,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot showing positive correlation between HOMA-IR and follicular fluid leptin (r=0.21, P=0.04). HOMA-IR: homeostasis-model assessment for insulin resistance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3437245/v1/1eac8f7c2b85acc4c25b6347.png"},{"id":46029136,"identity":"c8d576d5-8db5-4ad4-af82-14c98d3fcf83","added_by":"auto","created_at":"2023-11-07 17:48:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLoess fitting scatter plot showing the interaction of HOMA-IR and follicular fluid leptin on LB. HOMA-IR: homeostasis-model assessment for insulin resistance\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3437245/v1/49accd582be531d1b2074804.png"},{"id":49978719,"identity":"fe324cfc-60be-4677-8765-0e8a329a3d8e","added_by":"auto","created_at":"2024-01-22 15:08:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":806265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3437245/v1/25022c9d-af2b-4e90-9e8b-e962c7eef84b.pdf"},{"id":46029137,"identity":"74bd3540-ac68-49c1-82f5-7fddecaf3903","added_by":"auto","created_at":"2023-11-07 17:48:16","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":37389,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementray.docx","url":"https://assets-eu.researchsquare.com/files/rs-3437245/v1/8934fe28fc57c78c58e8756e.docx"}],"financialInterests":"","formattedTitle":"Revisiting the predictability of follicular fluid leptin and related adiposity measures for live birth in women scheduled for ICSI cycles, a prospective cohort study.","fulltext":[{"header":"Background","content":"\u003cp\u003eIn reproductive-aged women, obesity has been linked, in addition to the metabolic health risks, to ovulatory dysfunction, menstrual irregularities and suboptimal outcomes of variant fertility treatments (Practice Committee of the American Society for Reproductive Medicine, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gonzalez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eClinical metrics utilized as crucial descriptors for obesity include body mass index (BMI) and waist circumference (WC). However, body mass index considers total body weight and height without referring to body fat or central (abdominal) obesity. Also, waist circumference, although could accurately identify women with central obesity, it cannot precisely reflect preperitoneal fat. (Ponti et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eHence, different imaging tools for measuring preperitoneal fat have been proposed in reproductive aged women. Ultrasound has been determined as valid tool for assessment of intraabdominal preperitoneal fat compared to computerized tomography scan and magnetic resonance imaging. Moreover, ultrasound is more familiar in the clinicians' hands, inexpensive and devoid of radiation hazards (Suzuki et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Stolk et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ribeiro-Filho et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hamagawa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn earlier report demonstrated that preperitoneal fat thickness by ultrasound was superior to subcutaneous fat in predicting insulin resistance and other metabolic syndrome elements in non-specific population (Meri\u0026ntilde;o-Ibarra et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the level of the ovary, Ciavattini and colleagues found that increased preperitoneal fat as measured by ultrasound was associated with high reactive oxygen species in the follicular fluid and negatively correlated with oocyte and embryo quality (Ciavattini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, Obese anovulatory women with polycystic ovary syndrome (PCOS) who resume ovulation during a 6-month lifestyle program lost more visceral fat compared to the women who did not resume ovulation; despite similar subcutaneous fat loss in both groups (Kuchenbecker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeptin is a product of the adipose tissue. It is a hormone that plays a key role in the regulation of HPO axis to start puberty and maintain ovarian function (Silvestris et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn infertile women scheduled for In-vitro-fertilization (IVF), there are conflicting reports regarding the effect of follicular fluid (FF) leptin level on IVF outcomes. Some suggested deleterious effect of high leptin on embryo quality (Polyzos et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This controversy is evident in studies recruiting non-PCOS infertile population. Heterogenous methodology and endpoints stand beyond these incongruent observations. These studies did not analyze or correlate the findings in relation to different adiposity measures such as the preperitoneal fat, body fat index, or visceral adiposity index. And, few of them reported correlation with insulin resistance (Llaneza-Suarez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Neither body fat index (BFI) nor visceral adiposity index (VAI) have been assessed before in non-PCOS women for IVF.\u003c/p\u003e \u003cp\u003eBody fat index that compiled preperitoneal and subcutaneous fat in its calculation is a newly studied index in pregnant women as a predictability tool for gestational diabetes. (Nassr et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Benchahong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while visceral adiposity index has been deemed to predict insulin resistance in PCOS women (Oh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the current study, the researches targeted infertile women without PCOS to revisit the hypothesis whether FF leptin as adiposity biomarker could predict intracytoplasmic sperm injection (ICSI) outcomes in these women. Moreover, we integrated other adiposity measures in our evaluation (clinical, biochemical as well as sonographic) that have been proposed to be leptin-related. The reason for excluding PCOS women is to avoid the confounding effect of the pathophysiological mechanisms of PCOS on the study outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting:\u003c/h2\u003e \u003cp\u003eOur study is a prospective cohort study, conducted at Assisted Conception Unit, Department of Obstetrics and Gynecology, Women's Health Hospital, Assiut University, Egypt. The study was registered (NCT03778684, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.clinicaltrials.gov\" target=\"_blank\"\u003ewww.clinicaltrials.gov\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.clinicaltrials.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Recruitment was started on February 2019, and the study was completed on November 2022.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants:\u003c/h2\u003e \u003cp\u003eInfertile women indicated for intracytoplasmic sperm injection (ICSI) were eligible for enrollment if they were non-PCOS, aged between 18 and 35 years, anticipated normal responders, and had normal uterine cavity by trans-vaginal ultrasound. Non-PCOS women scheduled for ICSI comprised women with anovulation, unexplained infertility, tubal disease and male factor. Women with PCOS, those administering metformin, diabetic women, and poor responders based on Bologna criteria (Ferraretti et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) were not included in the study. The Rotterdam European Society for Human Reproduction and Embryology (ESHRE)/American Society for Reproductive Medicine (ASRM) criteria were used to define PCOS (Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group, 2004). Only one fresh ICSI transfer cycle for each participant was analyzed for the study outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculation:\u003c/h2\u003e \u003cp\u003eBased on a study by Llaneza-Suarez and colleagues (Llaneza-Suarez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) who determined a mean FF leptin concentration of 16.8 ng/mL (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 ng/ml), and 11.5 ng/ml (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 ng/ml) in non-PCOS women without live birth and with live birth respectively, 50 women were required as a sample size at 85% study power, two-sided significance level of 0.05, and effect size of 0.88. Owing to the large effect size, we were willing to test the hypothesis at a modest effect size of 0.65 at the same power and significance level, so the concluded sample size was 88 non-PCO women. Sample size calculation was done using G-Power 3.1.9.2 software program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEvaluated adiposity measures:\u003c/h2\u003e \u003cp\u003eClinical (BMI and WC), biochemical (VAI, lipid accumulation product, insulin resistance, and follicular fluid leptin) and sonographic (BFI) obesity-realted parameters were assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eClinical measures\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThey were evaluated at baseline and comprised waist circumference and body mass index. Waist circumference was measured by at the end of expiration by a tape applied to the skin of the participant at a plane perpendicular to the midline and passing through a point just above the top of the iliac crest (National Institutes of Health, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor body mass index (BMI), weight and height were measured while the participant is standing and wearing neither more than one layer of light clothes nor shoes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eBiochemical measures in serum and follicular fluid\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eBefore starting any ovarian stimulation, serum samples were taken following overnight fast for assessment of serum glucose, serum insulin and serum lipoproteins. Serum Glucose level was measured in mmol/L using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA. Serum fasting Insulin were measured in \u0026micro;IU/mL using Bioscience Human Insulin ELISA Kit (Catalog number :10801). Serum triglycerides and High-density lipoprotein cholesterol (HDL-C) levels were measured using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA.\u003c/p\u003e \u003cp\u003eOn day of oocyte retrieval, follicular fluid pooled from large follicles; 17 mm or more, containing cumulus-oocyte complex was selected for sampling. Fluids containing debris and blood were excluded. They were centrifugated at 1500 rpm for 5 minutes, then stored at -80 C until leptin measurement was performed using SinoGeneClon ELISA Kit (Catalog number: SG-10057). Follicular fluid leptin determination was by pg/ml. All biochemical tests were performed in the laboratory of Women's Health Hospital, Assiut University, Egypt.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSonographic measures:\u003c/h2\u003e \u003cp\u003eWe measured in this study the abdominal subcutaneous and preperitoneal fat utilizing the methodology validated in literature using ultrasound (Suzuki et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Stolk et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ribeiro-Filho et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Hamagawa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ciavattini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nassr et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The maximum preperitoneal and the minimum subcutaneous fat were the target measurements. They were conducted by the same researcher (The third author: AAM) using SONOACE R5 ultrasound machine with CN2-8 curved abdominal transducer (Samsung Medison Co., LTD). Four-months duration of capacity building for the researcher was achieved before study proposal submission to IRB through coupling with level-3 experience sonographer in order to ensure and maximize the quality of the scans.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eCalculations and benchmarking:\u003c/h2\u003e \u003cp\u003eBody mass index was calculated as weight (kg) divided by square of height (m\u003csup\u003e2\u003c/sup\u003e). According to World Health Organization (WHO), BMI was categorized as normal (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25- 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), or Obese (30 and above kg/m\u003csup\u003e2\u003c/sup\u003e) (World Health Organization, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify women with central obesity, waist circumference equal or more than 80 cm was used according to the International Diabetes Federation (IDF) and the report of WHO Expert Consultation on Obesity. (World Health Organization, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Alberti et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe homeostasis-model assessment for insulin resistance (HOMA-IR) was calculated using the equation: fasting insulin (\u0026micro;IU/mL) x glucose (mmol/L)) /22.5. Participants were designated to be insulin resistant if HOMA-IR was equal or more than 2.5 (Matthews et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Bo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNassr et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was the first to conclude and report on the body fat index formula. We utilized the same formula to calculate that new adiposity marker. It was calculated by multiplying pre-peritoneal fat (mm) and subcutaneous fat (mm), then dividing the product by height (cm) (Nassr et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSex-specific equations were employed to calculate visceral adiposity index and lipid accumulation product (Amato et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Taverna et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCycle management, Ovarian stimulation protocol and Embryo transfer:\u003c/h2\u003e \u003cp\u003eFor each woman, personal and fertility data were reported, and the indication for ICSI was affirmed. Preparation of the patients and selection of the ovarian stimulation protocol followed the standardized protocols in ICSI practice and was individualized based on each patient characteristics. Number of transferred embryos and day of embryo transfer were not uniform for all enrolled patients, nevertheless the same clinician conducted all transfers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy endpoints\u003c/h2\u003e \u003cp\u003eThe primary endpoint of the study was the probability of having a live birth (LB) per aspirated cycle as defined by the delivery of a live baby at 28 weeks of gestation or more. This is considered the standard definition in Egypt. We followed the standards of the Core Outcome Measure for Infertility Trials (COMMIT) initiative in reporting the primary and secondary endpoints (Duffy et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Time frame for reporting the outcomes was one fresh embryo transfer cycle. Enrolled women were contacted at the time of pregnancy test and estimated delivery date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed with the use of SPSS statistical package version 26.0 (IBM Corp, Armonk, NY, USA). The Kolmogorov-Smirnov test was used to determine data distribution. Normally distributed data are presented as mean (SD) however, abnormally distributed variables are presented as median (interquartile range (IQR)). Comparisons were conducted between women with and without live birth. Also, comparisons were done between women with and without central obesity as well as among the common three indications of ICSI in our cohort; unexplained, male, and other factors in order to explore if there was any hidden effect of the indication of ICSI on the study variables and outcomes. Other factors encompass tubal disease, endometriosis, anovulation, and combined factors. Based on the comparisons, when appropriate, means were compared with the use of Student t test or One-way ANOVA, and medians of non-parametric variables were compared utilizing Mann Whitney U test or Kruskal-Wallis test. Correlation analysis was conducted to determine correlation between the adiposity measures and ICSI cycle variables and outcomes. Predictive models were constructed using regression and receiver operating characteristic (ROC) curve analyses to evaluate the predictability of adiposity measures for cycle outcomes. P value of \u0026lt;\u0026thinsp;0.05 was considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eNinety-one women were enrolled and completed the study. Their median (IQR) age was 30 (7) years with 54.9% of the cohort (n\u0026thinsp;=\u0026thinsp;50) were in their thirties. In our cohort, the common indications for ICSI were male and unexplained factors in 41.8% (n\u0026thinsp;=\u0026thinsp;38) and 36.3% (n\u0026thinsp;=\u0026thinsp;33) of women respectively. Other indications of ICSI included tubal (9.9%), anovulatory (4.4%), endometriosis (1.1%), and combined (6.6%) factors. History of ICSI was found in 25 (27.5%) women while the remainder was undergoing their first ICSI cycle. Antagonist protocol was chosen for about two-thirds of the participants (n\u0026thinsp;=\u0026thinsp;60;65.9%). Eleven women (12.1%) had normal BMI while 33 (36.3%) and 47 (51.6%) were overweight and obese respectively. Based on waist circumference 52 (57.1%) women had central obesity.\u003c/p\u003e \u003cp\u003eThe total pregnancies were 31 (34.1%) ended in 28 live births: 25 term and 3 preterm births. There was one first trimester miscarriage, one second trimester miscarriage, and one ectopic pregnancy. Of live births, 11 cases were multiple pregnancies.\u003c/p\u003e \u003cp\u003eBased on the primary endpoint (live birth), comparisons between women with (n\u0026thinsp;=\u0026thinsp;28) and without live birth (n\u0026thinsp;=\u0026thinsp;63) are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Neither follicular fluid leptin concentration nor other adiposity measures were different between women with and without live birth.\u003c/p\u003e \u003cp\u003eIn Receiver Operating characteristic curve (ROC) and logistic regression analyses, none of the evaluated adiposity measures (WC, BMI, BFI, VAI, LAP, HOMA-IR, and FF Leptin) in our study was predictor for having a live birth. Area under the curve for WC, BMI, BFI, VAI, LAP, HOMA-IR, and Follicular fluid leptin was 0.57, 0.54, 0.55, 0.51, 0.53, 0.53, and 0.56, respectively.\u003c/p\u003e \u003cp\u003eStepwise multivariable logistic regression analysis, showed that the outcome of the prior cycle, ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for having live birth in our study (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInvestigating the relation among adiposity markers indicated that follicular fluid leptin was only correlated with HOMA-IR (Spearman's Correlation coefficient r\u0026thinsp;=\u0026thinsp;0.21, P\u0026thinsp;=\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This correlation is corroborated by the finding that HOMA-IR tends to be higher with increasing leptin tertile (P\u0026thinsp;=\u0026thinsp;0.047), when categorizing leptin values of the studied cohort into 3 tertiles: the first tertile is: \u0026lt; 15980.7 (n\u0026thinsp;=\u0026thinsp;31 women), the second tertile is : 15980.7 -to- 22130.9 (n\u0026thinsp;=\u0026thinsp;30 women), and the third tertile is: \u0026gt; 22130.9 (n\u0026thinsp;=\u0026thinsp;30 women).\u003c/p\u003e \u003cp\u003eLoess regression with Epanechnikov kernel fitting was done to demonstrate the interaction of follicular fluid leptin and HOMA-IR on live birth (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The scatter plot indicates that leptin increases with increased insulin resistance both in women with and without live birth. However, surprisingly in both groups, this positive correlation was lost or even reversed when HOMA-IR approached 5 or more.\u003c/p\u003e \u003cp\u003eComparing women with central obesity to their counterparts show that they were obese and overweight, respectively (median (IQR): 32 (6.9) vs 27.1 (6); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both groups were comparable regarding insulin resistance, follicular fluid leptin concentration, ICSI cycle characteristics and outcomes. Miscarriage cases occurred in women without central obesity while all preterm deliveries were reported in central obesity women. However, in deed, we found central obesity women more likely to have higher BFI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VAI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and LAP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplemental tables 1 and 2).\u003c/p\u003e \u003cp\u003eUnplanned subgroup post-hoc analysis according to the indication of ICSI showed that women in the male factor group had the lowest BMI (p\u0026thinsp;=\u0026thinsp;0.04), and through borderline significance; the least preperitoneal fat thickness (p\u0026thinsp;=\u0026thinsp;0.05), and the lowest fertilization rate (p\u0026thinsp;=\u0026thinsp;0.06). All cancelled transfers (n\u0026thinsp;=\u0026thinsp;4) were also in male factor group. Yet, follicular fluid leptin, the rest of the adiposity markers and cycle parameters as well as outcomes did not differ among women in this subgroup analysis (Supplemental tables 3 and 4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and adiposity measures of women with and without LB:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen with LB\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen without LB (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5 (9.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18- \u0026lt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25- \u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAMH (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrior ICSI cycles (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior failed cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior successful cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (76.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCauses of infertility (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (41.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnovulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZero\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e) (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass of body mass index (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist Circumference in cm (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (22.8)\u003c/p\u003e \u003cp\u003e55\u0026ndash;125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (22)\u003c/p\u003e \u003cp\u003e55\u0026ndash;115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of women with central obesity (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (58.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of insulin resistant women (\u0026ge;\u0026thinsp;2.5) (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHOMAIR (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreperitoneal fat in mm (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5.9)\u003c/p\u003e \u003cp\u003e(6\u0026ndash;18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (5.5)\u003c/p\u003e \u003cp\u003e(6.2\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubcutaneous fat in mm (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.5 (8.7)\u003c/p\u003e \u003cp\u003e(10.4\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7 (14.7)\u003c/p\u003e \u003cp\u003e(6.90\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody fat index (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisceral adiposity index (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid accumulation product (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.2 (23.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.4 (29.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollicular fluid Leptin* in pg/ml (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20336\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;8006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18493\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;6655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Results of follicular fluid samples for 6 cases (4 in women with LB group, and 2 in their counterparts) showed minimally than detected levels that were attributed to error in sampling or storage. So, leptin values for these 6 cases were computed through linear interpolation method for missed data calculation. HOMA-IR: homeostasis-model assessment for insulin resistance, BMI: body mass index, AMH: Anti-mullerian hormone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eICSI Cycle characteristics and outcomes in women with and without LB:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen with LB\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen without LB\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntral follicular count (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eToral gonadotropins dose (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3450 (1425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3600 (1500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal days of stimulation (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeak E2 (pg/ml) * (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2618 (1636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2738 (1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDay of trigger Progesterone (ng/ml) ** (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDay of trigger Endometrial thickness (mm) (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(Range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (2)\u003c/p\u003e \u003cp\u003e(8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2)\u003c/p\u003e \u003cp\u003e(7\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOocyte retrieved (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetaphase II oocytes (Median, IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmbryo transfer (n, %)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOne Embryo\u003c/p\u003e \u003cp\u003eTwo Embryos\u003c/p\u003e \u003cp\u003eThree Embryos\u003c/p\u003e \u003cp\u003eFour Embryos\u003c/p\u003e \u003cp\u003eCancelled Transfer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e12 (42.9%)\u003c/p\u003e \u003cp\u003e16 (57.1%)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.3%)\u003c/p\u003e \u003cp\u003e25 (39.7%)\u003c/p\u003e \u003cp\u003e25 (39.7%)\u003c/p\u003e \u003cp\u003e5 (7.9%)\u003c/p\u003e \u003cp\u003e4 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaturation index in %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e84.7%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e76.4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFertilization rate in %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen with good quality embryo transfer (at least one) (n,%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e27 (96.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e46 (73%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen who cryopreserved (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e19 (67.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e18 (30.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePeak E2 *, and Progesterone ** were analyzed for 77 and 65 cases, respectively due to missing data\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictability of cycle parameters for having a live birth\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICSI cycle parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior cycle result\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026ndash;3.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe ability to yield good quality Embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u0026ndash;1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe ability to have cryopreserved embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.01\u0026ndash;13.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOocyte maturation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;451.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMain findings:\u003c/h2\u003e \u003cp\u003eEmploying a prospective cohort study design, this study examined the follicular fluid leptin, along with a group of feasible clinical, biochemical, and sonographic adiposity markers as predictors of ICSI cycle outcomes in infertile non-PCOS women. Follicular fluid leptin was the basis for power analysis of the sample size. According to BMI, only 12.1% of women had normal BMI while the remainder is overweight and obese. None of the tested parameters (including follicular fluid leptin levels) could predict a live birth. Insulin resistance was the only adiposity marker that has been positively correlated to follicular fluid leptin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths:\u003c/h2\u003e \u003cp\u003eOnly FF leptin and insulin resistance were tested before in women without polycystic ovary syndrome. In the present work, we tested multiple markers that never were evaluated before in such patient cohort particularly; preperitoneal fat, body fat index and visceral adiposity index. Multiplicity of the evaluated markers was to seek explanations, detect superiority, and build combined predictors, in case of significance. The concept of testing multiple variants of predictors in overweight and obese women comes from observations that sole utility of leptin is not adequate to construct predictive model. Leptin to body mass index ratio has been reported to be superior to leptin alone as an IVF outcome predictor (Brannian et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDearth of literature reported live birth as a primary endpoint for the impact of FF leptin on ICSI. We followed our participants till delivery to reflect on live birth concurring with the published standards for reporting infertility trials (Harbin Consensus Conference Workshop Group, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Duffy et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eThere are limitations that should be pointed out. First, underrepresentation of normal BMI women in the enrolled cohort, which could affect the generalizability of conclusions and mitigate the discriminative threshold of the evaluated indices.\u003c/p\u003e \u003cp\u003eSecond, follicular fluid sampling like other studies presents a limitation (Jafarpour et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We used pooled follicular fluid which is still better than sampling just the first follicle. The ideal is to sample fluid from each follicle to conduct follicle-to-embryo tracking and sibling oocyte cohort analysis. However, it is difficult from the implementation point of view. Third, the study was powered for detection of leptin predictability but not for other tested parameters. Lack of comparable studies with similar study design presents a challenge in proposing assumptions during sample size calculation. Thus, the conclusions for the other parameters should be taken with caution. Fourth, although excluding PCOS women was justifiable, the performance of these indices to predict cycle outcomes in PCOS women remain elusive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eComment on the study adiposity measures:\u003c/h2\u003e \u003cp\u003eLeptin is a neuroendocrinal protein that exists in excess in obese women. Integrating follicular fluid leptin in our study is rationalized by; First: leptin is a reflection of the oocyte microenvironment; Second: leptin receptors and m-RNA are expressed in granulosa cells (L\u0026ouml;ffler et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), oocytes (Cioffi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Antczak and Van Blerkom, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and pre-implantation embryos (Antczak and Van Blerkom, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e); Third: leptin is depicted to regulate ovarian steroidogenesis, follicular growth and apoptosis, and oocyte maturation (Craig et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sirotkin et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Fourth: controversial reports exist for the impact of FF leptin on cycle outcomes in the studied infertile cohort (Mantzoros et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Anifandis et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jafarpour et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results concur with the results of a recent metanalysis evaluating (Jafarpour et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) 11 observational studies (266 pregnant and 552 non-pregnant cases). This metanalysis indicated that pregnancy was unrelated to leptin levels in the follicular fluid. Highlighting the dilemma of methodological heterogenicity in follicular fluid sampling and cycle outcome reporting, different groups of researches conveyed favorable cycle outcomes with low follicular fluid leptin concentrations. Mantzoros et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) sampled fluid from the first aspiration of the dominant follicle and demonstrated low FF leptin in pregnant women (11.9 vs 17 ng/ml) within three ART cycles. However, this could bias their conclusions as comparable follicular fluid microenvironment among all follicles cannot be guaranteed. Another study by Anifandis et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) did not explore the sampling details of follicular fluid and found women with peak estradiol range 1001\u0026ndash;2000 (n\u0026thinsp;=\u0026thinsp;53) yielded the highest pregnancy rate (35.8%) and had the lowest follicular fluid concentration (52 ng/ml). But, they did not test the predictability of follicular fluid leptin for clinical pregnancy after adjustment of peak serum estradiol. A study reporting live birth as an outcome found FF leptin sampled from the first dominant follicle predicted live birth at a concentration of 16 ng/ml with sensitivity and specificity of 78.3% and 54.2%, respectively (Llaneza-Suarez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Another group has addressed higher sensitivity and specificity of similar leptin concertation for the oocyte maturation when follicular fluid was pooled from the largest 3 follicles. (Hong et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBody mass index has been the benchmarking for decades in IVF/ICSI daily practice in pre-IVF/ICSI preparation of obese/overweight infertile women to achieve the optimum outcomes for the service stakeholders (Fertility NICE, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; ESHRE Guideline Group on Ovarian Stimulation, 2020). The contradictory conclusions about the effect of body mass index and weight reduction on the IVF/ICSI outcomes (Rittenberg et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mutsaerts et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Einarsson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sermondade et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) should encourage the clinicians to test other or new feasible adiposity predictors for the cycle outcomes such as visceral adiposity and body fat indices.\u003c/p\u003e \u003cp\u003eObesity and abdominal preperitoneal fat alter the metabolic milieu of follicular fluid in IVF women by increasing lipid peroxides and reducing total antioxidant capacity (Nasiri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hauck and Bernlohr, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ciavattini et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bacchetti et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Reflection of the preperitoneal fat on FF leptin in not addressed before. In our results, neither preperitoneal fat nor the new body fat index improved the predictability of FF leptin for cycle outcomes. Body fat index has been evaluated before in pregnant women and revealed to predict gestational diabetes at cut offs of 0.5 and 0.88 (Nassr et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Benchahong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eThe concept of preperitoneal fat measurement is warranted by evidence that it is a source of multiple inflammatory mediators contributing to insulin resistance and reproductive dysfunction (Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Fischer-Posovszky et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lumeng and Saltiel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The correlation between insulin resistance and leptin found in our study is observed in other studies (Llaneza-Suarez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and supported by the fact that insulin stimulates leptin secretion (Catteau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This correlation was unrelated to live birth in our women. Moreover, surprisingly, the increase in leptin with insulin resistance was lost at higher insulin resistance level denoting that a phenomenon of desensitization could be exhibited by leptin receptors at high insulin resistance levels. Further research is needed to elucidate this finding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current work could not find evidence that a correlation exists between follicular fluid leptin and live birth after ICSI cycles. Furthermore, prediction for live birth after IVF, could not be demonstrated when any of the tested parameters (clinical, biochemical, preperitoneal fat and BFI) was used. Further research should be warranted to evaluate the plausibility of these indices in all BMI categories of women without PCOS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003eThe Institutional Review Board (IRB) of Faculty of Medicine, Assiut University approved the study on January, 22, 2019 (IRB approval number: 17200286).\u0026nbsp;Every patient was informed about the steps of the study and a written informed consent was obtained from each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials :\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests :\u003c/strong\u003e the authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026apos; contribution:\u0026nbsp;\u003c/strong\u003e(1) the conception and design of the study or acquisition of data, or analysis and interpretation of data: Ahmed Abdelmagied,Mohammed K. Ali, Safwat Abdel-Rady, Ahmed A. Abdel-Alleem, Alaa Makhlouf, Azza Abo Elfadl (2) drafting the article or revising it critically for important intellectual content: Ahmed Abdelmagied, Mohammed K. Ali (3) final approval of the version to be submitted: Ahmed Abdelmagied, Alaa Makhlouf, Ahmed A. Abdel-Aleem, Safwat A. Mohamed, Azza Abo Elfadl , Mohammed K. Ali\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: The authors would like to thank Prof. Dina Habib for her role in the capacity building for the second author AAM before study start. Also, we would like thank Prof. Ehab Soud for his sincere linguistic revision of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlberti, K.G., Zimmet, P., Shaw, J. International Diabetes Federation: a consensus on Type 2 diabetes prevention. 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The Journal of clinical investigation. 2003;112(12):1821-30.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body fat index, central obesity, follicular fluid leptin, ICSI, insulin resistance, preperitoneal fat","lastPublishedDoi":"10.21203/rs.3.rs-3437245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3437245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOur research question is; could follicular fluid (FF) leptin solely or contemporaneously with other clinical, biochemical and sonographic adiposity measures predict the probability of having a live birth during ICSI cycles? .This is a prospective cohort study that enrolled infertile women without polycystic ovary syndrome scheduled for ICSI. At baseline, women had assessment of obesity using different metrics: clinical, serum biochemical, and sonographic. Clinical measures encompassed waist circumference and body mass index. Biochemical evaluation comprised assessment of homeostasis-model for insulin resistance, visceral adiposity index and lipid accumulation product. Preperitoneal and subcutaneous abdominal fat were measured using ultrasound and body fat index was calculated. On day of oocyte retrieval, pooled FF was sampled to assess FF leptin. Our primary outcome was live birth after one fresh embryo transfer cycle.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of Ninty-one women analyzed in this study, 28 have a live birth (30.8%). No difference in FF leptin concentration was found between women with and without live birth (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; 20336\u0026thinsp;\u0026plusmn;\u0026thinsp;8006 vs 18493\u0026thinsp;\u0026plusmn;\u0026thinsp;6655 pg/ml; P\u0026thinsp;=\u0026thinsp;0.2). None of the assessed adiposity markers was a predictor for live birth. Substantially, follicular fluid leptin was positively correlated with insulin resistance in women with and without live birth (r\u0026thinsp;=\u0026thinsp;0.21, P\u0026thinsp;=\u0026thinsp;0.04). In logistic regression analysis, the outcome of the prior cycle, ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for live birth in our study.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe present work could not find evidence that follicular fluid leptin, preperitoneal fat and other evaluated adiposity measures could impact live birth after ICSI cycles.\u003c/p\u003e","manuscriptTitle":"Revisiting the predictability of follicular fluid leptin and related adiposity measures for live birth in women scheduled for ICSI cycles, a prospective cohort study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-07 17:48:11","doi":"10.21203/rs.3.rs-3437245/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-11-04T21:03:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-11-01T14:00:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Middle East Fertility Society Journal","date":"2023-10-23T10:07:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-10-16T09:40:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Middle East Fertility Society Journal","date":"2023-10-13T17:19:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e355984f-2c03-4c51-933a-cd9ff5a06963","owner":[],"postedDate":"November 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-01-22T15:04:24+00:00","versionOfRecord":{"articleIdentity":"rs-3437245","link":"https://doi.org/10.1186/s43043-024-00164-y","journal":{"identity":"middle-east-fertility-society-journal","isVorOnly":false,"title":"Middle East Fertility Society Journal"},"publishedOn":"2024-01-17 15:01:20","publishedOnDateReadable":"January 17th, 2024"},"versionCreatedAt":"2023-11-07 17:48:11","video":"","vorDoi":"10.1186/s43043-024-00164-y","vorDoiUrl":"https://doi.org/10.1186/s43043-024-00164-y","workflowStages":[]},"version":"v1","identity":"rs-3437245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3437245","identity":"rs-3437245","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}